Text Summarization

Micropinion Generation: An Unsupervised Approach to Generating Ultra-Concise Summaries of Opinions

This paper presents a new unsupervised approach to generating ultra-concise summaries of opinions. We formulate the problem of generating such a micropinion summary as an optimization problem, where we seek a set of concise and non-redundant phrases that are readable and represent key opinions in text. We measure representativeness based on a modified mutual information function and model readability with an n-gram language model.

Opinosis: A Graph Based Approach to Abstractive Summarization of Highly Redundant Opinions

The Opinosis Summarization framework focuses on generating very short abstractive summaries from large amounts of text. These summaries can resemble micropinions or “micro-reviews” that you see on sites like twitter and four squares. The idea of the algorithm is to use a word graph data structure referred to as the Opinosis-Graph to represent the text to be summarized. Then, the resulting graph is repeatedly explored to find meaningful paths which in turn becomes candidate summary phrases.

What is ROUGE and how it works for evaluation of summaries?

ROUGE stands for Recall-Oriented Understudy for Gisting Evaluation. It is essentially of a set of metrics for evaluating automatic summarization of texts as well as machine translation. It works by comparing an automatically produced summary or translation against a set of reference summaries (typically human-produced). This article provides an intuitive explanation of how ROUGE works.